Part of the book series:Advances in Soft Computing ((AINSC,volume 49))
722Accesses
Abstract
In this paper we study a model to feature selection based on Ant Colony Optimization and Rough Set Theory. The algorithm looks for reducts by using ACO as search method and RST offers the heuristic function to measure the quality of one feature subset. Major results of using this approach are shown and others are referenced. Recently, runtime analyses of Ant Colony Optimization algorithms have been studied also. However the efforts are limited to specific classes of problems or simplified algorithm’s versions, in particular studying a specific part of the algorithms like the pheromone influence. From another point of view, this paper presents results of applying an improved ACO implementation which focuses on decreasing the number of heuristic function evaluations needed.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 17159
- Price includes VAT (Japan)
- Softcover Book
- JPY 21449
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Giráldez, R., Díaz-Díaz, N., Nepomuceno, I., Aguilar-Ruiz, J.S.: An Approach to Reduce the cost of Evaluation in Evolutionary Learning. In: Cabestany, J., Gonzalez Prieto, A., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 804–811. Springer, Heidelberg (2005)
Dorigo, M., DiCaro, G., Gambardella, L.M.: Ant colonies for discrete optimization. Artificial Life 5, 137–172 (1999)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Zhang, H., Sun, G.: Feature selection using tabu search method. Pattern Recognition Letters 35, 710–711 (2002)
Silver, E.: An overview of heuristic solution methods. Journal of the Operational Research Society 55, 936–956 (2004)
Jensen, R., Shen, Q.: Finding Rough Set Reducts with Ant Colony Optimization. In: UK Workshop on Computational Intelligence, 15–22 (2003)
Bello, R., Nowé, A.: A Model based on Ant Colony System and Rough Set Theory to Feature Selection. In: Genetic and Evolutionary Computation Conference (GECCO 2005), pp. 275–276 (2005)
Bello, R., Nowé, A.: Using Ant Colony System meta-heuristic and Rough Set Theory to Fea-ture Selection. In: The 6th Metaheuristics International Conference (MIC 2005), Vienna, Austria (2005)
Dorigo, M.: Scolarpedia, vol. 2 (2007)
Pawlak, Z.: Rough sets. International Journal of Information & Computer Sciences 11, 341–356 (1982)
Tay, F.E.S.L.: Economic and financial prediction using rough set model. European Jour-nal of Operational Research 141, 641–659 (2002)
Komorowski, J.a.P.Z.: Rough Set: A tutorial. Rough Fuzzy Hybridization: A new trend in decision making, 3–98 (1999)
Bell, D., Guan, J.: Computational methods for rough classification and discovery. Journal of ASIS 49, 403–414 (1998)
Wroblewski, J.: Genetic algorithms in decomposition and classification problems. In: Polkowski, L., Skowron, A. (eds.) Rough sets in Knowledge Discovery 1: Applications, pp. 472–492. Physica-Verlag
Wang, X.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28, 459–471 (2007)
Doerr, B., Neumann, F., Sudholt, D., Witt, C.: On the Runtime Analysis of the 1-ANT ACO Algorithm. In: GECCO 2007 (2007)
Neumann, F., Witt, C.: Runtime analysis of a simple Ant Colony Optimization algorithm. In: Asano, T. (ed.) ISAAC 2006. LNCS, vol. 4288, pp. 618–627. Springer, Heidelberg (2006)
Gutjahr, W.J.: First steps to the runtime complexity analysis of ant colony optimization. Computers and Operations Research (2007)
Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276, 51–81 (2002)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998),http://www.ics.uci.edu/~mlearn/MLRepository.html
Jensen, R., Shen, Q.: Fuzzy-Rough Data Reduction with Ant Colony Optimization. Fuzzy Set and System 149, 5–20 (2005)
Author information
Authors and Affiliations
Department of Computer Science, Universidad Central de Las Villas, Cuba
Yudel Gómez & Rafael Bello
Comp Lab, Department of Computer Science, Vrije Universiteit Brussel, Belgium
Ann Nowé & Frank Bosmans
- Yudel Gómez
You can also search for this author inPubMed Google Scholar
- Rafael Bello
You can also search for this author inPubMed Google Scholar
- Ann Nowé
You can also search for this author inPubMed Google Scholar
- Frank Bosmans
You can also search for this author inPubMed Google Scholar
Editor information
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gómez, Y., Bello, R., Nowé, A., Bosmans, F. (2009). Speeding-Up ACO Implementation by Decreasing the Number of Heuristic Function Evaluations in Feature Selection Problem. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_27
Download citation
Publisher Name:Springer, Berlin, Heidelberg
Print ISBN:978-3-540-85860-7
Online ISBN:978-3-540-85861-4
eBook Packages:EngineeringEngineering (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative